Search Results for "model-builder" - Page 51
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Showing 6997 open source projects for "model-builder"
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ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
...The goal of the project is to bring BERT-style models up to date with the capabilities of modern large language models while preserving the strengths of bidirectional encoder architectures used for tasks such as classification, retrieval, and semantic search. ModernBERT introduces architectural improvements that enhance both training efficiency and inference performance, making the model more suitable for modern large-scale machine learning pipelines. The repository also includes FlexBERT, a modular framework that allows developers to experiment with different encoder building blocks and configurations when constructing new models. -
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DevDocs by CyberAGI
Completely free, private, UI based Tech Documentation MCP server
DevDocs is an open-source documentation server designed to provide developers with a private, structured interface for browsing and interacting with technical documentation using AI tools. The system functions as a Model Context Protocol (MCP) server that allows large language models and developer assistants to access technical documentation in a structured and efficient way. Instead of sending entire documents to a language model, DevDocs organizes documentation into sections so that only the most relevant portions are retrieved during a query. ... -
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The LLM Evaluation guidebook
Sharing both practical insights and theoretical knowledge about LLM
...It compiles practical insights and theoretical knowledge gathered from real-world evaluation work, including experience managing the Open LLM Leaderboard and designing evaluation tools. The guidebook teaches developers how to design evaluation pipelines, select appropriate metrics, and interpret model performance results. It discusses multiple evaluation strategies, ranging from automated benchmarks to human evaluation and LLM-based evaluation techniques. The material also highlights the strengths and weaknesses of different evaluation methods, helping practitioners understand when and how to apply them. By organizing evaluation knowledge into structured sections, the project helps engineers and researchers build more reliable and trustworthy AI systems. -
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MatMul-Free LM
Implementation for MatMul-free LM
MatMul-Free LM is an experimental implementation of a large language model architecture designed to eliminate traditional matrix multiplication operations used in transformer networks. Since matrix multiplication is one of the most computationally expensive components of modern language models, the project explores alternative computational strategies that reduce hardware requirements while maintaining comparable performance. -
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Torch Pruning
DepGraph: Towards Any Structural Pruning
...Torch-Pruning physically removes parameters rather than masking them, which results in smaller and faster models during both training and inference. The toolkit supports a wide variety of architectures used in computer vision and large language models, making it a flexible solution for model compression tasks. -
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LLM Workflow Engine
Power CLI and Workflow manager for LLMs (core package)
...The platform allows users to interact with AI models directly from the terminal, enabling conversational AI access through shell commands and scripts. Instead of focusing solely on chat interactions, the system is built to embed LLM calls into larger automation pipelines where model outputs can drive decision making or trigger additional processes. Developers can construct structured workflows using configuration files and integrate them with tools such as Ansible playbooks or custom scripts to automate complex tasks. The engine supports multiple AI providers through a plugin architecture, allowing connections to services like OpenAI, Hugging Face, Cohere, or other compatible APIs. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
llms-from-scratch-cn is an educational open-source project designed to teach developers how to build large language models step by step using practical code and conceptual explanations. The repository provides a hands-on learning path that begins with the fundamentals of natural language processing and gradually progresses toward implementing full GPT-style architectures from the ground up. Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding... -
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The Alignment Handbook
Robust recipes to align language models with human and AI preferences
The Alignment Handbook is an open-source resource created to provide practical guidance for aligning large language models with human preferences and safety requirements. The project focuses on the post-training stage of model development, where models are refined after pre-training to behave more helpfully, safely, and reliably in real-world applications. It provides detailed training recipes that explain how to perform tasks such as supervised fine-tuning, preference modeling, and reinforcement learning from human feedback. The handbook also includes reproducible workflows for training instruction-following models and evaluating alignment quality across different datasets and benchmarks. ... -
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Intel LLM Library for PyTorch
Accelerate local LLM inference and finetuning
Intel LLM Library for PyTorch is an open-source acceleration library developed to optimize large language model inference and fine-tuning on Intel hardware platforms. Built as an extension of the PyTorch ecosystem, the library enables developers to run modern transformer models efficiently on Intel CPUs, GPUs, and specialized AI accelerators. The framework provides hardware-aware optimizations and low-precision computation techniques that significantly improve the performance of large language models while reducing memory consumption. ... -
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Token-Oriented Object Notation
Token-Oriented Object Notation (TOON)
...The format aims to reduce token overhead compared with traditional formats like JSON while remaining human-readable and structurally expressive. TOON represents the same data model as JSON but removes unnecessary syntax such as braces and quotes, relying instead on indentation and structured tokens to represent objects and arrays. This design allows prompts containing structured data to use significantly fewer tokens, which can reduce inference costs and improve efficiency in LLM applications. The project includes a formal specification, encoding rules, and reference implementations that developers can use to serialize and parse TOON data in their applications. -
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slime LLM
slime is an LLM post-training framework for RL Scaling
slime is an open-source large language model (LLM) post-training framework developed to support reinforcement learning (RL)-based scaling and high-performance training workflows for advanced LLMs, blending training and rollout modules into an extensible system. It offers a flexible architecture that connects high-throughput training (e.g., via Megatron-LM) with a customizable data generation pipeline, enabling researchers and engineers to iterate on new RL training paradigms effectively. ... -
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Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ... -
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CoAI.Dev
Next Generation AI One-Stop Internationalization Solution
CoAI.Dev is an open-source “one-stop” AIGC web application that combines a modern chat-first UI with a full backend for running, managing, and monetizing multiple AI model providers from one place. It is designed to support a wide range of LLM and image-generation backends (including OpenAI-compatible endpoints), while also providing an admin dashboard for user, subscription, and pricing controls, so it can be operated as a self-hosted AI product rather than just a personal playground. The app emphasizes cross-device conversation sync and sharing without requiring extra services like WebDAV, aiming to reduce setup friction for end users and increase retention for operators. ... -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. ... -
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Trae Agent
LLM-based agent for general purpose software engineering tasks
...“refactor this module,” “write a unit test,” “generate a REST API skeleton”), and then orchestrates tool-based workflows — such as file editing, shell/batch commands, code generation, code formatting or refactoring — to carry out complex engineering tasks. Under the hood, Trae Agent supports multiple LLM backends (so you can choose your preferred model provider), and comes with a modular architecture that makes it easy to study, extend, or modify. Because of its transparent, research-friendly design and detailed logging (trajectory recording), it is positioned not just as a productivity tool but also as a platform for researchers to explore, analyze, or extend AI-based code automation strategies. -
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Lingvo
Framework for building neural networks
Lingvo is a TensorFlow based framework focused on building and training sequence models, especially for language and speech tasks. It was originally developed for internal research and later open sourced to support reproducible experiments and shared model implementations. The framework provides a structured way to define models, input pipelines, and training configurations using a common interface for layers, which encourages reuse across different tasks. It has been used to implement state of the art architectures such as recurrent neural networks, Transformer models, variational autoencoder hybrids, and multi task systems. ... -
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VibeVoice ComfyUI
ComfyUI integration for Microsoft's VibeVoice text-to-speech model
...The project also introduces first-class LoRA support, making it possible to fine-tune and load custom LoRA adapters that modify voice identity or style while keeping the base VibeVoice model intact. -
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MCPJungle
Self-hosted MCP Gateway and Registry for AI agents
MCPJungle is a self-hosted gateway and registry for the Model Context Protocol (MCP), aimed at managing tool/integration servers for AI agents within organizations. It offers a “single source of truth” registry where developers can register MCP servers and the tools they provide, and MCP clients (such as AI agents) discover and consume those tools through one gateway endpoint. This greatly simplifies the architecture when you have many MCP servers; agents only need to connect to one gateway rather than multiple endpoints. ... -
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HunyuanWorld-Mirror
Fast and Universal 3D reconstruction model for versatile tasks
HunyuanWorld-Mirror focuses on fast, universal 3D reconstruction that can ingest varied inputs and produce multiple kinds of 3D outputs. The model accepts combinations of images, camera intrinsics and poses, or even depth cues, then reconstructs consistent 3D geometry suitable for downstream rendering or editing. The pipeline emphasizes both speed and flexibility so creators can go from casual captures to assets without elaborate capture rigs. Outputs can include point clouds, estimated camera parameters, and other 3D representations that plug into typical graphics workflows. ... -
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nanochat
The best ChatGPT that $100 can buy
...The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. The code is written to be read—concise training loops, transparent configs, and minimal wrappers—so you can audit each step, tweak it, and rerun without getting lost in framework indirection. -
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Visual Blocks
Visual Blocks for ML is a Google visual programming framework
...Under the hood it leans on web-friendly runtimes (e.g., WebGPU/WebGL/WebNN or TensorFlow.js backends) to execute pipelines locally, which is great for demos, teaching, and privacy-sensitive prototypes. The block abstraction encourages modularity: you can package a preprocessor, a model, and a postprocessor as a reusable composite for others to slot into their graphs. Because everything lives in the browser, sharing is as simple as exporting a project or link, and collaborators can experiment without installing toolchains. For educators and product teams alike, Visual Blocks reduces the distance from idea to interactive proof-of-concept by turning ML diagrams. -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained inspection and modification after training. Its modular design includes tools for tree manipulation, named axes, and declarative neural network construction. ... -
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DBHub
Universal database MCP server connecting to MySQL, PostgreSQL
DBHub is a universal database gateway that implements the MCP server interface so assistants and IDEs can explore and query databases through typed tools. It supports multiple transports—stdio for desktop clients and HTTP for networked scenarios—making it flexible to embed or deploy. Configuration is environment-variable driven, with a DSN and per-engine settings covering Postgres, MySQL, MariaDB, SQL Server, and SQLite. Operational flags include read-only mode, row limits, and even SSH... -
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MCP Shrimp Task Manager
Shrimp Task Manager is a task tool built for AI Agents
Shrimp Task Manager is an MCP server that converts natural-language requests into structured development tasks with dependencies, status, and style/format rules—built for agents that reason step-by-step. It emphasizes chain-of-thought and reflection loops, allowing an assistant to plan, refine, and re-prioritize work like a human project assistant. The server exposes typed tools so clients can create tasks, link prerequisites, record progress, and enforce writing or coding standards for... -
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Claude Code Subagents Command Collection
Claude Code Subagents & Commands Collection + CLI Tool
This repository aggregates a large set of specialized subagents and slash commands designed for Claude Code, giving developers domain-focused “teammates” they can summon on demand. Each subagent is defined by a concise role, tools, and behaviors, and ships as Markdown you can drop into your .claude/agents/ directory. The collection targets common developer workflows such as scaffolding, refactoring, test writing, documentation, security checks, and project management. It includes a CLI...
